{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f4587fa3-7741-4e5f-a0b4-6091badefffb",
   "metadata": {},
   "source": [
    "使用重复元素的⽹络"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "563bf5c0-c4c8-426d-a26a-1fcb6130f5bf",
   "metadata": {},
   "source": [
    "组成规律:连续使用多个相同的填充为1、窗口形状为3×3的卷积层,随后接上一个步幅为2、窗口形状为2×2的最大池化层。卷积层保持输入的高和宽不变,而池化层则对其减半。我们使\r\n",
    "⽤ vgg_block 函数来实现这个基础的VGG块，它可以指定卷积层的数量和输⼊输出通道数。"
   ]
  },
  {
   "attachments": {
    "e36b73ab-778d-40c9-a925-b16ac550571a.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "fcd77136-de7f-4f05-8a38-6b1452a6482c",
   "metadata": {},
   "source": [
    "![image.png](attachment:e36b73ab-778d-40c9-a925-b16ac550571a.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6553c94c-57b3-4fa4-9d86-89594946f8fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "import d2lzh_pytorch as d2l\n",
    "\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "def vgg_block(num_convs, in_channels, out_channels):\n",
    "    '''\n",
    "    使用卷积层加上最大池化层，卷积层保持高和宽不变，池化层则对其减半\n",
    "    '''\n",
    "    blk = []\n",
    "    for i in range(num_convs):\n",
    "        if i == 0:\n",
    "            blk.append(nn.Conv2d(in_channels, out_channels, kernel_size=3,padding=1))\n",
    "        else:\n",
    "            blk.append(nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1))\n",
    "        blk.append(nn.ReLU())\n",
    "    blk.append(nn.MaxPool2d(kernel_size=2, stride=2))   #这里使宽高减半\n",
    "    return nn.Sequential(*blk)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1092a28-aeba-4406-9885-d03a67940e56",
   "metadata": {},
   "source": [
    "1.2VGG网络"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "525153c0-8f0f-4a6b-b465-5a84ac59473f",
   "metadata": {},
   "source": [
    "VGG网络由卷积层模块后接全连接层模块构成。卷积层模块串联数个vgg_block ，其超参数由变量 conv_arch 定义。该变量指定每个VGG块里卷积层的个数和输入输出的通道数。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3376789e-c0ba-4845-b0d7-24f44c5bd3af",
   "metadata": {},
   "source": [
    "conv_arch 变量定义了一个卷积神经网络的架构，其中每个元组 (repeat, in_channels, out_channels) 代表一个卷积块（block）的参数：\n",
    "\n",
    "repeat：该层卷积的重复次数。\n",
    "in_channels：输入通道数。\n",
    "out_channels：输出通道数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "424ef82f-8300-4c37-87e9-6177168fa1ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_arch = ((1,1,64),(1,64,128),(2,128,256),(2,256,512),(2,512,512))\n",
    "# 经过5个vgg_block, 宽⾼会减半5次, 变成 224/32 = 7\n",
    "fc_features = 512 * 7 * 7   #c * w * h\n",
    "fc_hidden_units = 4096  #任意设置,全连接层神经元的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "56466d72-e5f6-4839-bfec-36f5ba499546",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vgg(conv_arch, fc_features, fc_hidden_units=4096):\n",
    "    net = nn.Sequential()\n",
    "    for i, (num_convs, in_channels, out_channels) in enumerate(conv_arch):\n",
    "        #每经过一个vgg_block都会使宽高减半\n",
    "        net.add_module('vgg_block_' + str(i + 1),vgg_block(num_convs, in_channels, out_channels))\n",
    "    #全连接层\n",
    "    net.add_module(\"fc\", nn.Sequential(d2l.FlattenLayer(),\n",
    "                                      nn.Linear(fc_features,fc_hidden_units),\n",
    "                                      nn.ReLU(),\n",
    "                                      nn.Dropout(0.5),\n",
    "                                      nn.Linear(fc_hidden_units,fc_hidden_units),\n",
    "                                      nn.ReLU(),\n",
    "                                      nn.Dropout(0.5),\n",
    "                                      nn.Linear(fc_hidden_units, 10)))\n",
    "    return net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "20f77ac9-496f-4619-9df7-be231c66931e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vgg_block_1 output_shape:  torch.Size([1, 64, 112, 112])\n",
      "vgg_block_2 output_shape:  torch.Size([1, 128, 56, 56])\n",
      "vgg_block_3 output_shape:  torch.Size([1, 256, 28, 28])\n",
      "vgg_block_4 output_shape:  torch.Size([1, 512, 14, 14])\n",
      "vgg_block_5 output_shape:  torch.Size([1, 512, 7, 7])\n",
      "fc output_shape:  torch.Size([1, 10])\n"
     ]
    }
   ],
   "source": [
    "net = vgg(conv_arch, fc_features, fc_hidden_units)\n",
    "X = torch.rand(1, 1, 224, 224)\n",
    "\n",
    "# named_children获取⼀级⼦模块及其名字(named_modules会返回所有⼦模块,包括⼦模块的⼦模块)\n",
    "for name, blk in net.named_children():\n",
    "    X = blk(X)\n",
    "    print(name, 'output_shape: ',X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e62e63f0-ca59-4f0f-bbd9-70826075620a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (vgg_block_1): Sequential(\n",
      "    (0): Conv2d(1, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU()\n",
      "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (vgg_block_2): Sequential(\n",
      "    (0): Conv2d(8, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU()\n",
      "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (vgg_block_3): Sequential(\n",
      "    (0): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU()\n",
      "    (2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU()\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (vgg_block_4): Sequential(\n",
      "    (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU()\n",
      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU()\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (vgg_block_5): Sequential(\n",
      "    (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU()\n",
      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU()\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (fc): Sequential(\n",
      "    (0): FlattenLayer()\n",
      "    (1): Linear(in_features=3136, out_features=512, bias=True)\n",
      "    (2): ReLU()\n",
      "    (3): Dropout(p=0.5, inplace=False)\n",
      "    (4): Linear(in_features=512, out_features=512, bias=True)\n",
      "    (5): ReLU()\n",
      "    (6): Dropout(p=0.5, inplace=False)\n",
      "    (7): Linear(in_features=512, out_features=10, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "ratio = 8\n",
    "small_conv_arch = [(1, 1, 64//ratio), (1,64//ratio, 128//ratio), (2, 128//ratio, 256//ratio), (2, 256//ratio, 512//ratio), (2, 512//ratio, 512//ratio)]\n",
    "net = vgg(small_conv_arch, fc_features // ratio, fc_hidden_units // ratio)\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bf5283ce-228d-464a-a5a1-fb93f0d63d83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training on cuda\n",
      "epoch 1, loss 0.2155, train acc 0.922, test acc 0.918, time 74.1 sec\n",
      "epoch 2, loss 0.1994, train acc 0.927, test acc 0.920, time 70.2 sec\n",
      "epoch 3, loss 0.1864, train acc 0.932, test acc 0.921, time 68.6 sec\n",
      "epoch 4, loss 0.1741, train acc 0.936, test acc 0.922, time 69.8 sec\n",
      "epoch 5, loss 0.1625, train acc 0.941, test acc 0.919, time 70.9 sec\n"
     ]
    }
   ],
   "source": [
    "batch_size = 64\n",
    "#resize是在保证图片不变的情况下,缩放图片的大小.\n",
    "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)\n",
    "\n",
    "lr, num_epochs = 0.001, 5\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n",
    "d2l.train_ch5(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cf6b977-2ec6-436b-b546-3349c1f07d12",
   "metadata": {},
   "source": [
    "如何将 VGG 应用于 PointNet++ 分割模型\n",
    "PointNet++ 是基于点云的深度学习模型，主要用于 3D 点云分类和分割任务。要将 VGG 网络的思想应用到 PointNet++，可以考虑以下方式：\n",
    "\n",
    "(1) 作为特征提取模块\n",
    "VGG 适用于 2D 图像，而 PointNet++ 处理 3D 点云数据，因此可以在点云数据投影到 2D 视图后，使用 VGG 提取 2D 层面的特征。\n",
    "方法：\n",
    "先将 3D 点云投影到多个 2D 视角（如鸟瞰图、侧视图）。\n",
    "使用 VGG 作为 Backbone 提取 2D 视图的特征，并将其融合到 PointNet++ 中。\n",
    "(2) 作为局部特征提取模块\n",
    "PointNet++ 通过 Set Abstraction 进行层次化特征提取，可以借鉴 VGG 深度网络的思想，将 3×3 卷积核的思想应用到 3D 点云邻域特征提取。\n",
    "方法：\n",
    "在 PointNet++ 的局部特征提取模块中，引入 VGG 类似的多个小感受野卷积层（如 MLP 结构）。\n",
    "通过多个 1D 卷积（1×1 Conv）或者动态卷积来学习局部特征。\n",
    "(3) 结合 VGG 的迁移学习\n",
    "由于 VGG 在 2D 图像上的预训练能力较强，可以将其应用于点云到图像的转换任务：\n",
    "例如，在 RGB-D 点云数据（包含深度信息）中，利用 VGG 处理 RGB 图像，PointNet++ 处理深度信息，再进行特征融合。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d14b1cb-9dff-4cb3-b894-eb503bd776ba",
   "metadata": {},
   "source": [
    "优点:可以设计层次更深的网络,可以捕捉更复杂的特征.同时使用更小的卷积核,减少参数提高模型的精确度."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "703132ed-14c8-49ea-8f30-a6a622a1aaf0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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